Skip to content

This page was last updated on 2024-07-17 08:46:24 UTC

Recommendations for the article B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index
visibility_off Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation Yifei Zong, D. Barajas-Solano, A. Tartakovsky 2024-07-05 ArXiv 0 41
visibility_off Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data C. Bonneville, C. Earls 2021-08-05 ArXiv 12 21
visibility_off Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion Andrew Pensoneault, Xueyu Zhu 2023-03-13 ArXiv 1 17
visibility_off Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems Yifei Zong, D. Barajas-Solano, A. Tartakovsky 2023-12-11 ArXiv 1 41
visibility_off Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang, K. Garikipati 2021-01-13 ArXiv 10 32
visibility_off Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Xu Liu, Wenjuan Yao, Wei Peng, Weien Zhou 2022-05-14 Neurocomputing 10 40
visibility_off Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng, Liu Yang, Zhiping Mao, J. Ferrandis, G. Karniadakis 2021-06-08 J. Comput. Phys. 45 127
Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index